As wireless communication systems are making transition from voice-only communications to interactive Internet data and multi-media applications, the desire for higher data rate transmissions are increasing tremendously. With the increase in wireless devices, future technologies will face spectral crowding, and coexistence of wireless devices will be a major problem. Considering the limited bandwidth availability, accommodating the demand for higher capacity and data rates is a challenging task, requiring innovative technologies that can offer new ways of exploiting the available radio spectrum.
Cognitive radio is a viable solution for spectrum scarcity problem that arises with increased number of users and applications that require higher data rates such as video and Internet by enabling opportunistic spectrum usage (Mitola, J. and J. Maguire, G. Q., “Cognitive radio: making software radios more personal,” IEEE Personal Commun. Mag., vol. 6, no. 4, pp. 13-18, August 1999). One crucial aspect of cognitive radio is related to its ability to autonomously identify the available unused spectrum and to adapt to environmental and spectral characteristics of a geographical location.
In cognitive radio, primary users have higher priority or legacy rights than secondary users on the spectrum usage. Conversely, secondary users exploit the idle spectrum as to not interfere with primary users. Therefore, secondary users need to have the capability to sense the spectrum usage reliably. Spectrum sensing requires obtaining the spectrum characteristics across multiple dimensions such as time, space, frequency, and code.
Matched filtering is the optimum method for detection of primary users. However, traditional matched filtering requires the cognitive radio to demodulate the received signal and requires perfect knowledge of the primary users signaling features. Moreover, since cognitive radio needs receivers for all signal types, traditional matched-filtering is practically difficult to implement. Spectrum sensing can also be performed by correlating the received signal with a known copy of itself, known as waveform-based sensing (H. Tang, “Some physical layer issues of wide-band cognitive radio systems,” in Proc. IEEE mt. Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Md., USA, November 2005, pp. 151-159). However, this method is only applicable to systems with known repeating patterns such as wireless metropolitan area network (WMAN) signals (IEEE Standard for Local and Metropolitan area networks Part 16, The Institute of Electrical and Electronics Engineering, Inc. Std. IEEE 802.16E-2005, 2005). Another method for detection of primary user transmission is cyclostationarity feature detection, where cyclostationarity features of the received signal are exploited (D. Cabric and R. W. Brodersen, “Physical layer design issues unique to cognitive radio systems,” in Proc. IEEE mt. Symposium on Personal, Indoor and Mobile Radio Commun., vol. 2, Berlin, Germany, September 2005, pp. 759-763). These features are caused by the periodicity in the signal or in its statistics (mean, autocorrelation etc.). Instead of power spectral density (PSD), cyclic correlation function is used for detecting the signals present in a given spectrum. Cyclostationarity based methods can differentiate noise from the primary users. Finally, energy detector-based spectrum sening is common because of low hardware complexity, robust performance, and blind primary users' signal characterization. The presence of a signal can be detected by comparing the output of the energy detector with a threshold which depends on the noise floor. However, energy detection suffers from an inability to differentiate interference from primary users' signals and poor performance under low signal-to-noise ratios (SNRs) (H. Tang, “Proc. IEEE mt. Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Md., USA, November 2005, pp. 151-159).
Cognitive radio devices must identify unused signal spectrum in a fast and efficient way. Conventional algorithms sense the spectrum without exploiting all the properties of primary users. The a priori information about transmission properties of possible primary users can be leveraged for developing a framework for spectrum sensing.
Current spectrum estimation techniques do not utilize most of the a priori knowledge about primary users. This knowledge about center frequencies and bandwidth of this type of a signal along with other transmission characteristics can be exploited to identify the presence of a transmission and thus to improve spectrum sensing accuracy. The features that describe devices can be obtained by investigating the received signal. These features then can be matched to the a priori sets of known parameters. By finding the exact transmission characteristics of primary users, possible estimation errors due to sensing algorithm and noise can be removed.